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Journal ArticleDOI

Fault classification and fault signature production for rolling element bearings in electric machines

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TLDR
In this paper, the authors introduced the notion of categorizing bearing faults as either single-point defects or generalized roughness, which separate bearing faults according to the fault signatures that are produced rather than by the physical location of the fault.
Abstract
Most condition monitoring techniques for rolling element bearings are designed to detect the four characteristic fault frequencies This has lead to the common practice of categorizing bearing faults according to fault location (ie, inner race, outer race, ball, or cage fault) While the ability to detect the four characteristic fault frequencies is necessary, this approach neglects another important class of faults that arise in many industrial settings This research introduces the notion of categorizing bearing faults as either single-point defects or generalized roughness These classes separate bearing faults according to the fault signatures that are produced rather than by the physical location of the fault Specifically, single-point defects produce the four predictable characteristic fault frequencies while faults categorized as generalized roughness produce unpredictable broadband changes in the machine vibration and stator current Experimental results are provided from bearings failed in situ via a shaft current These results illustrate the unpredictable and broadband nature of the effects produced by generalized roughness bearing faults This issue is significant because a successful bearing condition monitoring scheme must be able to reliably detect both classes of faults

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Citations
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Journal ArticleDOI

Review of condition monitoring of rotating electrical machines

TL;DR: Condition monitoring of rotating electrical machinery has received intense research interest for more than 30 years as mentioned in this paper, and the state of the art is reviewed in the following ways: survey developments in condition monitoring of machines, mechanically and electrically, over the last 30 years; put that work in context alongside the known failure mechanisms; review those developments which have proved successful and identify areas of research which require attention in the future to advance the subject.
Journal ArticleDOI

Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring

TL;DR: New models for the influence of rolling-element bearing faults on induction motor stator current are described, based on two effects of a bearing fault: the introduction of a particular radial rotor movement and load torque variations caused by the bearing fault.
Journal ArticleDOI

A review on data-driven fault severity assessment in rolling bearings

TL;DR: In this article, a review of fault severity assessment of rolling bearing components is presented, focusing on data-driven approaches such as signal processing for extracting proper fault signatures associated with the damage degradation, and learning approaches that are used to identify degradation patterns with regards to health conditions.
Journal ArticleDOI

Online wind turbine fault detection through automated SCADA data analysis

TL;DR: The results presented demonstrate that the interpretation techniques can provide performance assessment and early fault identification, thereby giving the operators sufficient time to make more informed decisions regarding the maintenance of their machines.
Journal ArticleDOI

Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks

TL;DR: This work presents a novel monitoring scheme applied to diagnose bearing faults that takes into account the detection of distributed defects, such as roughness, and analyzes the most significant statistical-time features calculated from vibration signal.
References
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Proceedings ArticleDOI

Motor bearing damage detection using stator current monitoring

TL;DR: In this article, the authors used motor current spectral analysis to detect rolling-element bearing damage in induction machines, where the bearing failure modes were reviewed and bearing frequencies associated with the physical construction of the bearings were defined.
Journal ArticleDOI

Neural-network-based motor rolling bearing fault diagnosis

TL;DR: Simulation and real-world testing results obtained indicate that neural networks can be effective agents in the diagnosis of various motor bearing faults through the measurement and interpretation of motor bearing vibration signatures.
Journal ArticleDOI

Experimentally generating faults in rolling element bearings via shaft current

TL;DR: In this paper, a method was developed that employs an externally applied shaft current to initiate and progress a bearing fault in an accelerated timeframe, which can be used to evaluate the performance of various bearing condition monitoring schemes.
Proceedings ArticleDOI

Experimentally generating faults in rolling element bearings via shaft current

TL;DR: In this paper, a method was developed that employs an externally applied shaft current to initiate and progress a bearing fault in an accelerated timeframe, and the experimental results showed that the act of removing and replacing test bearings drastically alters the machine vibration and stator current spectral characteristics.
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